scholarly journals Multistage graph problems on a global budget

2021 ◽  
Vol 868 ◽  
pp. 46-64
Author(s):  
Klaus Heeger ◽  
Anne-Sophie Himmel ◽  
Frank Kammer ◽  
Rolf Niedermeier ◽  
Malte Renken ◽  
...  
Keyword(s):  
2017 ◽  
Vol 51 (1) ◽  
pp. 261-266 ◽  
Author(s):  
Édouard Bonnet ◽  
Vangelis Th. Paschos
Keyword(s):  

2001 ◽  
Vol 63 (4) ◽  
pp. 639-671 ◽  
Author(s):  
Uriel Feige ◽  
Joe Kilian
Keyword(s):  

1982 ◽  
Vol 25 (9) ◽  
pp. 659-665 ◽  
Author(s):  
Francis Y. Chin ◽  
John Lam ◽  
I-Ngo Chen

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.


Science ◽  
1995 ◽  
Vol 270 (5244) ◽  
pp. 1905-1905
Keyword(s):  

Author(s):  
Ji Youn Lee ◽  
Hee-Woong Lim ◽  
Suk-In Yoo ◽  
Byoung-Tak Zhang ◽  
Tai Hyun Park

Author(s):  
Yaping Xiao ◽  
Jennifer A. Logan ◽  
Daniel J. Jacob ◽  
Rynda C. Hudman ◽  
Robert Yantosca ◽  
...  
Keyword(s):  

2008 ◽  
pp. 958-961
Author(s):  
Camil Demetrescu ◽  
Giuseppe F. Italiano

Author(s):  
Elham Hatef ◽  
Hadi Kharrazi ◽  
Ed VanBaak ◽  
Marc Falcone ◽  
Lindsey Ferris ◽  
...  

Maryland Department of Health (MDH) has been preparing for alignment of its population health initiatives with Maryland’s unique All-Payer hospital global budget program. In order to operationalize population health initiatives, it is required to identify a starter set of measures addressing community level health interventions and to collect interoperable data for those measures. The broad adoption of electronic health records (EHRs) with ongoing data collection on almost all patients in the state, combined with hospital participation in health information exchange (HIE) initiatives, provides an unprecedented opportunity for near real-time assessment of the health of the communities. MDH’s EHR-based monitoring complements, and perhaps replaces, ad-hoc assessments based on limited surveys, billing, and other administrative data. This article explores the potential expansion of health IT capacity as a method to improve population health across Maryland.First, we propose a progression plan for four selected community-wide population health measures: body mass index, blood pressure, smoking status, and falls-related injuries. We then present an assessment of the current and near real-time availability of digital data in Maryland including the geographic granularity on which each measure can be assessed statewide. Finally, we provide general recommendations to improve interoperable data collection for selected measures over time via the Maryland HIE. This paper is intended to serve as a high- level guiding framework for communities across the US that are undergoing healthcare transformation toward integrated models of care using universal interoperable EHRs.


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